statistical-graphics

bayesplot R package for plotting Bayesian models

A gallery of animations in statistics and utilities to create animations

shinystan R package and ShinyStan GUI

SUpporting GRaphics with R for ANalysing Time Series

'Visualization in Bayesian workflow' by Gabry, Simpson, Vehtari, Betancourt, and Gelman. (JRSS discussion paper and code)

A Toolkit for Interactive Statistical Data Visualization

Modern Statistical Graphics （《现代统计图形》的附加包）

manage ordinations and render biplots in a tidyverse workflow

data science, statistics and machine learning

Interactive Jupyter notebooks showcasing data visualization with Matplotlib and Seaborn for learners and data practitioners.

Visualizing the Covid-19 pandemic with doubling rates

📖 现代统计图形（第二版） Modern Statistical Graphics (Second Edition)

Materials for a workshop in June 2025

📊 CRAN-published R package, seaborn for R, built on ggplot2 — the same API and defaults, visually indistinguishable plots, and every plot is a ggplot you can extend.

R scripts drawing a hierarchical edge bundling diagram of Mariana Trench project factor/method connections with ggraph/igraph (bundled splines over a hierarchy layout). Fig. 22 in Lemenkova (2019), Geodesy and Cartography 45(2):57-84.

In this course, you will learn how to use Seaborn, a Python library for producing statistical graphics. You will learn how to use Seaborn's sophisticated visualization tools to analyze your data, create informative visualizations, and communicate your results with ease.

R script drawing lollipop (stem-and-dot) charts and diverging bar charts of Mariana Trench profiles ranked by normalized value with above/below-average grouping (ggplot2). Figs 19-20 in Lemenkova (2019), Geodesy and Cartography 45(2):57-84.

R script for agglomerative hierarchical cluster analysis of bathymetric profiles: z-score scaling, Euclidean and correlation distance, average-linkage hclust, dendextend/magrittr dendrogram styling, tanglegram comparison and pvclust AU/BP bootstrap p-values. Mariana Trench; Lemenkova (2020), Carpathian JECE 13(1):5-12.

Python/Seaborn scripts computing and plotting Pearson, Spearman and Kendall correlation matrices as heatmaps, demonstrated on Mariana Trench morphometry (with Nigeria band-correlation variants). Reusable scientific-plotting example.

Python script performing principal component analysis (PCA) with scikit-learn and visualising the components with seaborn, on Mariana Trench morphometry. Reusable data-analysis example.